Extending ProbLog with Continuous Distributions

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Logic Tensor Networks for Semantic Image Interpretation (2017, background): “The area of Statistical Relational Learning (SRL), or statistical AI (StarAI), seeks to combine learning from data with symbolic knowledge in the presence of uncertainty [Wang and Domingos, 2008; Bröcheler et al., 2012; Gutmann et al., 2010; Diligenti et al., 2015; Rocktaschel et al., 2015; Ravkic e”
Learning relational dependency networks in hybrid domains (2015, background): “…interest in designing hybrid SRL formalisms such as Hybrid Markov Logic Networks (HMLNs) (Wang and Domingos 2008), Hybrid ProbLog (HProbLog) (Gutmann et al. 2011), Continuous BayesianLogicPrograms (CBLPs) (Kersting andDeRaedt 2001), LearningModuloTheories (LMT) (Teso et al. 2013) and Hybrid…”
Probabilistic (logic) programming concepts (2015, background): “Gutmann et al (2010) introduce Hybrid ProbLog, an extension of ProbLog to continuous distributions, but their inference approach based on exact backward reasoning and discretization severely limits the use of such distributions.”
Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation (2017, background): “Examples of hybrid SRL frameworks are Hybrid Markov Logic Networks [26], Hybrid ProbLog [11], Continuous Bayesian Logic Programs [12], Learning Modulo Theories [24], Hybrid Probabilistic Relational [17], and hybrid relational dependency networks [20].”